Nucleocytoplasmic Transport of RNA-Binding Proteins Regulates Neural Stem Cell Fates 
Bibliographic record
Abstract
Abstract The formation of the cerebral cortex requires balanced expansion and differentiation of neural progenitor cells, the fate choice of which requires regulation at many steps of gene expression. As progenitor cells often exhibit transcriptomic stochasticity, the ultimate output of cell fate-determining genes must be carefully controlled at the post-transcriptional level, but how this is orchestrated is poorly understood. Here we report that de novo missense variants in an RNA-binding protein CELF2 cause human cortical malformations and perturb neural progenitor cell fate decisions in mice by disrupting the nucleocytoplasmic transport of CELF2. In self-renewing neural progenitors, CELF2 is localized in the cytoplasm where it binds and coordinates mRNAs that encode cell fate regulators and neurodevelopmental disorder-related factors. The translocation of CELF2 into the nucleus releases mRNAs for translation and thereby triggers neural progenitor differentiation. Our results reveal a mechanism by which transport of CELF2 between discrete subcellular compartments orchestrates an RNA regulon to instruct cell fates in cerebral cortical development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".